AI project

# -*- coding: utf-8 -*-
"""Untitled0.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VXsDpUtbDoa8VfiJ7zf1LicPno-AB1Ku
"""

!pip install google-cloud-aiplatform
!pip install langchain
!pip install chromadb
!pip install pytube
!pip install youtube-transcript-api
!pip install gradio
from google.cloud import aiplatform

from google.colab import auth as google_auth
google_auth.authenticate_user()

import vertexai
PROJECT_ID = "fleet-reserve-406414" #enter your project id here
vertexai.init(project=PROJECT_ID)

from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import VertexAI

llm = VertexAI(
model_name="text-bison@001",
max_output_tokens=256,
temperature=0.1,
top_p=0.8,
top_k=40,
verbose=True,
)

from langchain.embeddings import VertexAIEmbeddings

# Embedding
EMBEDDING_QPM = 100
EMBEDDING_NUM_BATCH =5
embeddings = VertexAIEmbeddings(
    requests_per_minute=EMBEDDING_QPM,
    num_instances_per_batch=EMBEDDING_NUM_BATCH,
)

loader = YoutubeLoader.from_youtube_url("https://www.youtube.com/watch?v=A8jyW_6hCGU&t=161s", add_video_info=True)
result = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=0)
docs = text_splitter.split_documents(result)
print(f"# of documents = {len(docs)}")

db = Chroma.from_documents(docs, embeddings)

retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})

qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True)

def sm_ask(question, print_results=True):
  video_subset = qa({"query": question})
  context = video_subset
  prompt = f"""
  Answer the following question in a detailed manner, using information from the text below. If the answer is not in the text,say I dont know and do not generate your own response.

  Question:
  {question}
  Text:
  {context}

  Question:
  {question}

  Answer:
  """
  parameters = {
  "temperature": 0.1,
  "max_output_tokens": 256,
  "top_p": 0.8,
  "top_k": 40
  }
  response = llm.predict(prompt, **parameters)
  return {
  "answer": response

  }

import gradio as gr
def get_response(input_text):
  response = sm_ask(input_text)
  return response

grapp = gr.Interface(fn=get_response, inputs="text", outputs="text")
grapp.launch()